Data exploration

From Lizard guts: Naxos

Kate Culhane, Dept. of Ecology, Evolution, and Marine Biology, UC Santa Barbara
kathrynculhane@ucsb.edu

---

SUMMARY

Exploring the data

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DEPENDENCIES

##### PACKAGES #####

library(tidyverse) # data manipulation & visualization
library(kableExtra) # pretty tables
library(vegan) # community analyses
library(goeveg) # scree plot for NMDS

##### SOURCE CODE #####

source("R_scripts/source_scales.R") # scales to use in ggplots
source("R_scripts/source_factor_levels.R") # functions to assign factor levels

##### DATA #####

# Abundance data
prey <- read_csv("output/00_prey.csv") %>% # sticky + pitfall samples
  fl_sample()
diet <- read_csv("output/00_diet_inverts.csv") %>% # diet samples
  fl_sample()
inverts <- read_csv("output/00_inverts_all.csv") %>% # ALL invert samples
  fl_sample()

# Site by species matrices
prey_matrix <- read_csv("output/00_prey_matrix.csv") # sticky + pitfall samples
diet_matrix <- read_csv("output/00_diet_matrix.csv") # diet samples
inverts_matrix <- read_csv("output/00_inverts_matrix.csv") # ALL invert samples

Abundance

Number of invert individuals per order

inverts %>% 
  group_by(order, sample_type) %>% 
  summarise(n = sum(count)) %>% 
  pivot_wider(names_from = sample_type, values_from = n) %>% 
  replace(., is.na(.), 0) %>% 
  mutate(trap_total = pitfall + sticky,
         diet = cell_spec(diet,
                          bold = ifelse(diet > 10, "TRUE", "FALSE"),
                          color = ifelse(diet > 10, "red", "black"))) %>% 
  arrange(-trap_total) %>% 
  relocate(diet, .after = last_col()) %>% 
  kbl(escape = FALSE) %>% 
  kable_minimal(full_width = FALSE, position = "left")

order	sticky	pitfall	trap_total	diet
diptera	1808	102	1910	9
hymenoptera	102	1290	1392	129
coleoptera	71	427	498	119
hemiptera	121	148	269	218
acari	9	158	167	14
collembola	1	116	117	0
araneae	15	62	77	76
embioptera	55	10	65	0
raphidioptera	19	0	19	1
thysanura	0	16	16	4
isopoda	0	11	11	9
coleoptera_larvae	0	10	10	0
psocoptera	10	0	10	0
orthoptera	6	3	9	28
chilopoda	0	7	7	0
pseudoscorpiones	0	6	6	2
lepidoptera	3	2	5	10
opilliones	0	4	4	0
lepidoptera_larvae	0	3	3	0
gastropoda	1	1	2	33
homoptera	1	0	1	0
isoptera	0	1	1	0
neuroptera	0	1	1	0
blattodea	0	0	0	5
egg	0	0	0	10
larvae	0	0	0	54
odonata	0	0	0	1

• Number of invert individuals summed across samples
• Orders with >10 individuals in diet samples highlighted in red

Trapping samples

# Abundance by order
prey %>% 
  ggplot(aes(x = sample_type, y = count)) +
  geom_jitter(aes(color = sample_type),
              width = 0.1, size = 1.5) +
  scale_color_manual(values = scol_trap) +
  geom_boxplot(fill = NA, outlier.shape = NA) +
  facet_wrap(~ fct_reorder(order, count, .fun = sum, .desc = TRUE),
             scales = "free_y") +
  theme_classic() +
  theme(legend.position = "NA") +
  labs(x = "Trap type", y = "Abundance (indv/trap)")

• Number of invert individuals trapped per order
• Each point represents a trap (12 trap pairs/site, 9 sites)

Diet samples

# Abundance by order
diet %>% 
  # Data wrangling
  group_by(order) %>% 
  mutate(n = length(count)) %>% 
  # Plot
  ggplot(aes(x = fct_reorder(order, count, .fun = sum, .desc = TRUE), y = count)) +
  geom_jitter(width = 0.1, size = 1.5) +
  geom_boxplot(fill = NA, outlier.shape = NA) +
  geom_text(aes(y = 10, label = paste("n =", n)),
            stat = "unique", angle = 90, size = 3, hjust = 0) +
  scale_y_continuous(expand = c(0,0),
                     limits = c(0,11.5),
                     breaks = c(0,2,4,6,8,10)) +
  theme_classic() +
  theme(legend.position = "NA",
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  labs(x = "Order", y = "Abundance (indv/lizard)")

• Number of invert individuals consumed per order
• Each point represents a stomach sample from a single lizard (148 lizards)
• Four outliers not plotted for hemiptera: 48, 22, 22, 17

NMDS

Invert trap data

# Wrangle matrix
m_p <- as.matrix(prey_matrix[-c(1:4)])
rownames(m_p) <- prey_matrix$sample
m_p_hell <- decostand(m_p, 'hellinger') # Hellinger transformation
m_p_meta <- select(prey_matrix, sample, site, trap, sample_type) %>% # metadata
  fl_sample()

# Scree plot to check stress per number of dimensions
dimcheckMDS(m_p_hell, distance = "bray", autotransform = FALSE, k = 10)

# Create NMDS ordination
ord_p <- metaMDS(m_p_hell,
                 distance = 'bray', # use Bray-Curtis distances
                 autotransform = FALSE, # already manually transformed the matrix
                 k = 3, # number of dimensions
                 trymax = 1000)

# Stress plot
stressplot(ord_p)

# Plot ordination
par(mar = c(4,4,1,1))
plot(ord_p, display = 'species', type = 'n')
points(ord_p, display = 'sites', pch = 19,
       col = scale_trap[m_p_meta$sample_type])
ordiellipse(ord_p, groups = m_p_meta$sample_type,
            label = TRUE, col = scale_trap, lwd = 2)
text(ord_p, display = 'species')

• Each dot is a trap
• Number of dimensions = ord_p$ndim
• Stress = round(ord_p$stress,3)

Diet + trapping data

# Wrangle matrix
m_i <- as.matrix(inverts_matrix[-c(1:3)])
rownames(m_i) <- inverts_matrix$sample
m_i_hell <- decostand(m_i, 'hellinger') # Hellinger transformation
m_i_meta <- select(inverts_matrix, sample, site, sample_type) # metadata

# Scree plot to check stress per number of dimensions
dimcheckMDS(m_i_hell, distance = "bray", autotransform = FALSE, k = 10)

# Create NMDS ordination
ord_i <- metaMDS(m_i_hell,
                 distance = 'bray', # use Bray-Curtis distances
                 autotransform = FALSE, # already manually transformed the matrix
                 k = 2, # number of dimensions
                 trymax = 1000)

# Stress plot
stressplot(ord_i)

# Plot ordination
par(mar = c(4,4,1,1))
plot(ord_i, display = 'species', type = 'n', xlim = c(-0.15, 0.15))
points(ord_i, display = 'sites', pch = 19,
       col = scol_sample[m_i_meta$sample_type])
ordiellipse(ord_i, groups = m_i_meta$sample_type,
            label = TRUE, col = "black", lwd = 2)
text(ord_i, display = 'species')

• Each dot is a trap
• Number of dimensions = ord_f$ndim
• Stress = round(ord_f$stress,3)

---

SESSION INFO

devtools::session_info()

## - Session info --------------------------------------------------------------------------------------
##  setting  value                       
##  version  R version 4.1.0 (2021-05-18)
##  os       Windows 10 x64              
##  system   i386, mingw32               
##  ui       RStudio                     
##  language (EN)                        
##  collate  English_United States.1252  
##  ctype    English_United States.1252  
##  tz       America/Los_Angeles         
##  date     2021-08-11                  
## 
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## [2] C:/Users/kathr/AppData/Local/Temp/RtmpQzFpij/renv-system-library
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##  P -- Loaded and on-disk path mismatch.